Improve printing for FLUX distributed training

flux/dreambooth.py

@@ -261,19 +261,23 @@ if __name__ == "__main__":
     generate_progress_images(0, flux, args)
 
     grads = None
-    losses = []
+    batch_cnt = 0
+    total_loss = 0
     tic = time.time()
     for i, batch in zip(range(args.iterations), trainer.iterate(args.batch_size)):
         loss, grads = step(*batch, guidance, grads, (i + 1) % args.grad_accumulate == 0)
-        mx.eval(loss, grads, state)
+        total_loss = total_loss + loss
-        losses.append(loss.item())
+        batch_cnt += 1
+        mx.eval(total_loss, grads, state)
 
-        if (i + 1) % 10 == 0:
+        if (i + 1) % 10 == 0 and mx.distributed.init().rank() == 0:
             toc = time.time()
             peak_mem = mx.metal.get_peak_memory() / 1024**3
+            total_loss = mx.distributed.all_sum(total_loss, stream=mx.cpu)
+            total_loss = total_loss.item() / batch_cnt
             print(
-                f"Iter: {i + 1} Loss: {sum(losses) / 10:.3f} "
+                f"Iter: {i + 1} Loss: {total_loss:.3f} "
-                f"It/s: {10 / (toc - tic):.3f} "
+                f"It/s: {batch_cnt / (toc - tic):.3f} "
                 f"Peak mem: {peak_mem:.3f} GB",
                 flush=True,
             )
@@ -285,7 +289,8 @@ if __name__ == "__main__":
             save_adapters(f"{i + 1:07d}_adapters.safetensors", flux, args)
 
         if (i + 1) % 10 == 0:
-            losses = []
+            total_loss = 0
+            batch_cnt = 0
             tic = time.time()
 
     save_adapters("final_adapters.safetensors", flux, args)

llms/mlx_lm/evaluate.py

@@ -10,7 +10,7 @@ import logging
 import os
 from importlib.metadata import version
 from pathlib import Path
-from typing import Optional
+from typing import Optional, Union
 
 import lm_eval
 import mlx.core as mx
@@ -20,10 +20,11 @@ from lm_eval.api.model import LM
 from lm_eval.api.registry import register_model
 from tqdm import tqdm
 
-from .models.base import create_causal_mask
 from .models.cache import make_prompt_cache
 from .utils import load, stream_generate
 
+PAD = 0
+
 
 def _len_longest_common_prefix(a, b):
     l = 0
@@ -42,14 +43,31 @@ def _rstrip_until(s, untils):
     return s[: min(f)]
 
 
-def _pad_inputs(inputs):
+def _pad_inputs(
-    lengths = np.array([len(x) for x in inputs])
+    inputs,
-    maxlen = lengths.max()
+    maxlen,
-    padded = np.stack(
+    genlen=0,
-        [np.pad(x, (0, maxlen - len(x))) for x in inputs],
+    pad_left=False,
+    pad_multiple=32,
+    truncate=False,
+):
+    # pad the prompts to the left with at least genlen tokens.
+    actual_maxlen = max(len(p) for p in inputs) + genlen
+    if actual_maxlen > maxlen:
+        if not truncate:
+            raise ValueError("Inputs are too long.")
+        else:  # drop begining
+            actual_maxlen = maxlen
+            inputs = [p[max(0, len(p) - maxlen) :] for p in inputs]
+    if pad_multiple > 0:
+        maxlen = (actual_maxlen + pad_multiple - 1) // pad_multiple
+        maxlen *= pad_multiple
+    assert PAD == 0
+    lr = np.array((1, 0) if pad_left else (0, 1))
+    return np.stack(
+        [np.pad(np.array(x, np.int32), lr * (maxlen - len(x))) for x in inputs],
         axis=0,
     )
-    return mx.array(padded), mx.array(lengths)
 
 
 @register_model("mlxlm")
@@ -65,33 +83,32 @@ class MLXLM(LM):
         self._batch_size = batch_size
         self._model, self.tokenizer = load(path_or_hf_repo)
         self._max_tokens = max_tokens or self.tokenizer.model_max_length
-        self.use_chat_template = use_chat_template and (
+        self.use_chat_template = use_chat_template or (
             self.tokenizer.chat_template is not None
         )
 
-    def _score_fn(self, inputs, step_size: int = 64):
+    def _score_fn(self, inputs, tokenize=True, step_size=32):
-        inputs, lengths = _pad_inputs(inputs)
+        if tokenize:
+            inputs = self._tokenize(inputs)
+        inputs = _pad_inputs(inputs, self._max_tokens, truncate=False)
+        inputs = mx.array(inputs)
         inputs, targets = inputs[..., :-1], inputs[..., 1:]
 
         cache = make_prompt_cache(self._model)
 
+        mask = targets != PAD
+
         scores, is_greedy = [], []
         for i in range(0, inputs.shape[1], step_size):
-            inp = inputs[:, i : i + step_size]
+            logits = self._model(inputs[:, i : i + step_size], cache=cache)
-            T = inp.shape[1]
 
-            offset = cache[0].offset
-            mask = create_causal_mask(T, offset, lengths=lengths)
-            mask = mask == 0
-
-            logits = self._model(inp, cache=cache, mask=mask)
             log_probs = nn.log_softmax(logits.astype(mx.float32))
-
             score = mx.take_along_axis(
                 log_probs, targets[:, i : i + step_size, mx.newaxis], axis=-1
             )[..., 0]
-            ig = targets[:, i : i + step_size] == mx.argmax(logits, axis=-1)
+            ig = mask[:, i : i + step_size] * (
-            ig = mx.where(mx.arange(T) + offset < lengths[:, None], ig, False)
+                targets[:, i : i + step_size] == mx.argmax(logits, axis=-1)
+            )
 
             mx.eval(score, ig)
             mx.metal.clear_cache()
@@ -102,32 +119,38 @@ class MLXLM(LM):
         scores = mx.concatenate(scores, axis=1)
         is_greedy = mx.concatenate(is_greedy, axis=1)
 
-        return scores, lengths, is_greedy
+        return scores, mask.sum(axis=-1), is_greedy
 
-    def _loglikelihood(self, texts, score_spans=None):
+    def _loglikelihood(self, texts, score_spans=None, tokenize=True):
-        all_scores = mx.zeros(len(texts))
+        # sort by length to get batches with little padding.
-        all_is_greedy = mx.zeros(len(texts), dtype=mx.bool_)
+        sorted_indices = sorted(range(len(texts)), key=lambda i: -len(texts[i]))
-        for i in tqdm(range(0, len(texts), self._batch_size)):
+        sorted_inputs = [texts[sorted_indices[i]] for i in range(len(texts))]
-            batch = texts[i : i + self._batch_size]
+        sorted_spans = None
-            scores, lengths, is_greedy = self._score_fn(batch)
-
-            ind = np.arange(scores.shape[-1])
         if score_spans is not None:
-                spans = score_spans[i : i + self._batch_size]
+            sorted_spans = [score_spans[sorted_indices[i]] for i in range(len(texts))]
-                lengths = [end - start for start, end in spans]
-                masks = mx.array(
-                    np.array([(ind >= start) & (ind < end) for start, end in spans])
-                )
-            else:
-                masks = ind[None] < lengths[:, None]
 
-            scores = (masks * scores).sum(axis=-1)
+        results = []
-            is_greedy = (masks * is_greedy).sum(axis=-1)
+        for i in tqdm(range(0, len(sorted_inputs), self._batch_size)):
+            batch = sorted_inputs[i : i + self._batch_size]
+            scores, length, is_greedy = self._score_fn(batch, tokenize=tokenize)
+            for j in range(len(batch)):
+                if sorted_spans is None:  # full sequence score
+                    mask = mx.arange(scores[j].shape[-1]) < length
+                    score = (scores[j].astype(mx.float32) * mask).sum(axis=-1)
+                    ig = (is_greedy[j].astype(mx.int32) * mask).sum(axis=-1)
+                else:  # subsequence score
+                    start, end = sorted_spans[i + j]
+                    score = scores[j][start:end].astype(mx.float32).sum()
+                    ig = is_greedy[j][start:end].astype(mx.int32).sum()
+                    length = end - start
 
-            all_scores[i : i + self._batch_size] = scores
+                results.append((score.item(), ig.item(), length))
-            all_is_greedy[i : i + self._batch_size] = is_greedy == lengths
 
-        return all_scores, all_is_greedy
+        # reorder the outputs
+        inv_sort = np.argsort(sorted_indices)
+        results = [results[inv_sort[i]] for i in range(len(results))]
+
+        return results
 
     def _tokenize(self, texts):
         return [
@@ -199,53 +222,16 @@ class MLXLM(LM):
                 + "completion longer than context."
             )
 
-        num_results = len(shortened)
-
-        # sort by length to get batches with little padding.
-        sorted_indices = sorted(range(len(shortened)), key=lambda i: -len(shortened[i]))
-        shortened = [shortened[i] for i in sorted_indices]
-        completion_spans = [completion_spans[i] for i in sorted_indices]
-
-        group = mx.distributed.init()
-
-        # split strided so we have approximately the same lengths on each node
-        shortened = shortened[group.rank() :: group.size()]
-        completion_spans = completion_spans[group.rank() :: group.size()]
-
         # model scoring, returns num_requests x (logp, is_greedy, length).
-        scores, is_greedy = self._loglikelihood(
+        results = self._loglikelihood(
             shortened,
             score_spans=completion_spans,
+            tokenize=False,
         )
-
+        return [(r[0], r[1] == r[2]) for r in results]
-        # all gather the results across groups
-        if group.size() > 1:
-            per_group = int(np.ceil(num_results / group.size()))
-            scores = mx.pad(scores, ((0, per_group - len(scores)),))
-            is_greedy = mx.pad(is_greedy, ((0, per_group - len(is_greedy))))
-            scores = mx.distributed.all_gather(scores[mx.newaxis], stream=mx.cpu)
-            is_greedy = mx.distributed.all_gather(is_greedy[mx.newaxis], stream=mx.cpu)
-            scores = scores.T.reshape(-1)
-            is_greedy = is_greedy.T.reshape(-1)
-
-        scores = np.array(scores[:num_results])
-        is_greedy = np.array(is_greedy[:num_results])
-
-        results = [(score, ig) for score, ig in zip(scores, is_greedy)]
-        inv_sort = np.argsort(sorted_indices)
-        results = [results[inv_sort[i]] for i in range(len(inv_sort))]
-        return results
 
     tokenizer_name = lm_eval.models.huggingface.HFLM.tokenizer_name
-
+    apply_chat_template = lm_eval.models.huggingface.HFLM.apply_chat_template
-    def apply_chat_template(
-        self, chat_history: list[dict[str, str]], add_generation_prompt: bool = True
-    ) -> str:
-        if len(chat_history) == 0:
-            return ""
-        return lm_eval.models.huggingface.HFLM.apply_chat_template(
-            chat_history, add_generation_prompt
-        )
 
     def loglikelihood_rolling(self, requests) -> list[float]:
         """Compute full log-likelihood of a string, with no truncation, for perplexity computation
@@ -282,9 +268,8 @@ class MLXLM(LM):
         logging.info(
             "Estimating loglikelihood rolling for %d sequences." % len(requests)
         )
-        inputs = self._tokenize([req.args[0] for req in requests])
+        inputs = [req.args[0] for req in requests]
-        scores, _ = self._loglikelihood(inputs)
+        return [t[0] for t in self._loglikelihood(inputs)]
-        return scores.tolist()
 
     def generate_until(self, requests) -> list[str]:
         """Generate greedily until a stopping sequence
@@ -347,7 +332,7 @@ def main():
     )
     parser.add_argument(
         "--limit",
-        default=None,
+        default=1.0,
         help="Limit the number of examples per task.",
         type=float,
     )
@@ -361,8 +346,11 @@ def main():
     )
     parser.add_argument(
         "--apply-chat-template",
-        action="store_true",
+        action=argparse.BooleanOptionalAction,
-        help="Specifies whether to apply a chat template to the prompt.",
+        help="Specifies whether to apply a chat template to the prompt. If "
+        "the model has a chat template, this defaults to `True`, "
+        "otherwise `False`.",
+        default=None,
     )
     args = parser.parse_args()
 

llms/mlx_lm/examples/pipeline_generate.py

@@ -22,11 +22,6 @@ import mlx.core as mx
 from mlx_lm import load, stream_generate
 
 parser = argparse.ArgumentParser(description="LLM pipelined inference example")
-parser.add_argument(
-    "--model",
-    default="mlx-community/DeepSeek-R1-3bit",
-    help="HF repo or path to local model.",
-)
 parser.add_argument(
     "--prompt",
     "-p",
@@ -42,7 +37,9 @@ parser.add_argument(
 )
 args = parser.parse_args()
 
-model, tokenizer = load(args.model, lazy=True)
+model_repo = "mlx-community/DeepSeek-V3-3bit"
+
+model, tokenizer = load(model_repo, lazy=True)
 
 messages = [{"role": "user", "content": args.prompt}]
 prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)

llms/mlx_lm/lora.py

@@ -78,7 +78,6 @@ def build_parser():
         "--train",
         action="store_true",
         help="Do training",
-        default=None,
     )
     parser.add_argument(
         "--data",
@@ -136,7 +135,6 @@ def build_parser():
         "--test",
         action="store_true",
         help="Evaluate on the test set after training",
-        default=None,
     )
     parser.add_argument(
         "--test-batches",
@@ -158,7 +156,6 @@ def build_parser():
         "--grad-checkpoint",
         action="store_true",
         help="Use gradient checkpointing to reduce memory use.",
-        default=None,
     )
     parser.add_argument("--seed", type=int, help="The PRNG seed")
     return parser

llms/mlx_lm/models/deepseek_v3.py

@@ -400,8 +400,6 @@ class DeepseekV3Model(nn.Module):
 
         pipeline_rank = self.pipeline_rank
         pipeline_size = self.pipeline_size
-        # Hack to avoid time-outs during prompt-processing
-        dist_stream = mx.cpu if h.shape[1] > 1 else mx.gpu
         if mask is None:
             mask = create_attention_mask(h, cache)
 
@@ -409,21 +407,18 @@ class DeepseekV3Model(nn.Module):
             cache = [None] * len(self.layers)
 
         # Receive from the previous process in the pipeline
-
         if pipeline_rank < pipeline_size - 1:
-            h = mx.distributed.recv_like(h, (pipeline_rank + 1), stream=dist_stream)
+            h = mx.distributed.recv_like(h, (pipeline_rank + 1))
 
         for layer, c in zip(self.layers, cache):
             h = layer(h, mask, c)
 
         # Send to the next process in the pipeline
         if pipeline_rank != 0:
-            h = mx.distributed.send(
+            h = mx.distributed.send(h, (pipeline_rank - 1) % pipeline_size)
-                h, (pipeline_rank - 1) % pipeline_size, stream=dist_stream
-            )
 
         # Broadcast h while keeping it in the graph
-        h = mx.distributed.all_gather(h, stream=dist_stream)[: h.shape[0]]
+        h = mx.distributed.all_gather(h)[: h.shape[0]]
 
         return self.norm(h)
 

llms/mlx_lm/models/internlm3.py

@@ -1,241 +0,0 @@
-# Copyright © 2023-2024 Apple Inc.
-
-from dataclasses import dataclass
-from typing import Any, Dict, Optional, Tuple, Union
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
-
-
-@dataclass
-class ModelArgs(BaseModelArgs):
-    model_type: str
-    hidden_size: int
-    num_hidden_layers: int
-    intermediate_size: int
-    num_attention_heads: int
-    rms_norm_eps: float
-    vocab_size: int
-    bias: bool = False
-    qkv_bias: bool = False
-    max_position_embeddings: int = 32768
-    num_key_value_heads: int = None
-    rope_theta: float = 10000
-    rope_traditional: bool = False
-    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
-    tie_word_embeddings: bool = False
-
-    def __post_init__(self):
-        if self.num_key_value_heads is None:
-            self.num_key_value_heads = self.num_attention_heads
-
-        if self.rope_scaling:
-            required_keys = {"factor", "rope_type"}
-            if not all(key in self.rope_scaling for key in required_keys):
-                raise ValueError(f"rope_scaling must contain keys {required_keys}")
-
-            if self.rope_scaling["rope_type"] not in ["linear", "dynamic"]:
-                raise ValueError(
-                    "rope_scaling 'rope_type' currently only supports 'linear' or 'dynamic"
-                )
-
-
-class DynamicNTKScalingRoPE(nn.Module):
-    """Implements the rotary positional encoding with Dynamic NTK scaling."""
-
-    def __init__(
-        self,
-        dims: int,
-        max_position_embeddings: int = 2048,
-        traditional: bool = False,
-        base: float = 10000,
-        scale: float = 1.0,
-    ):
-        super().__init__()
-        self.max_position_embeddings = max_position_embeddings
-        self.original_base = base
-        self.dims = dims
-        self.traditional = traditional
-        self.scale = scale
-
-    def extra_repr(self):
-        return f"{self.dims}, traditional={self.traditional}, max_position_embeddings={self.max_position_embeddings}, scaling_factor={self.scaling_factor}"
-
-    def __call__(self, x, offset: int = 0):
-        seq_len = x.shape[1] + offset
-        if seq_len > self.max_position_embeddings:
-            base = self.original_base * (
-                (self.scale * seq_len / self.max_position_embeddings) - (self.scale - 1)
-            ) ** (self.dims / (self.dims - 2))
-        else:
-            base = self.original_base
-
-        return mx.fast.rope(
-            x,
-            self.dims,
-            traditional=self.traditional,
-            base=base,
-            scale=self.scale,
-            offset=offset,
-        )
-
-
-class Attention(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-
-        dim = args.hidden_size
-        qkv_bias = args.qkv_bias
-        self.n_heads = n_heads = args.num_attention_heads
-        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
-        self.n_kv_groups = n_heads // args.num_key_value_heads
-
-        self.head_dim = head_dim = args.hidden_size // n_heads
-        self.scale = head_dim**-0.5
-
-        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=qkv_bias)
-        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=qkv_bias)
-        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=qkv_bias)
-        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=qkv_bias)
-
-        rope_scale = (
-            1 / args.rope_scaling["factor"]
-            if args.rope_scaling is not None
-            and args.rope_scaling["rope_type"] == "linear"
-            else 2.0
-        )
-
-        self.rope = DynamicNTKScalingRoPE(
-            head_dim,
-            max_position_embeddings=args.max_position_embeddings,
-            traditional=args.rope_traditional,
-            base=args.rope_theta,
-            scale=rope_scale,
-        )
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Any] = None,
-    ) -> mx.array:
-        B, L, D = x.shape
-
-        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
-
-        # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-
-        if cache is not None:
-            queries = self.rope(queries, offset=cache.offset)
-            keys = self.rope(keys, offset=cache.offset)
-            keys, values = cache.update_and_fetch(keys, values)
-        else:
-            queries = self.rope(queries)
-            keys = self.rope(keys)
-
-        output = scaled_dot_product_attention(
-            queries, keys, values, cache=cache, scale=self.scale, mask=mask
-        )
-
-        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
-        return self.o_proj(output)
-
-
-class MLP(nn.Module):
-    def __init__(self, dim, hidden_dim, bias):
-        super().__init__()
-        self.gate_proj = nn.Linear(dim, hidden_dim, bias=bias)
-        self.down_proj = nn.Linear(hidden_dim, dim, bias=bias)
-        self.up_proj = nn.Linear(dim, hidden_dim, bias=bias)
-
-    def __call__(self, x) -> mx.array:
-        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.self_attn = Attention(args)
-        self.mlp = MLP(args.hidden_size, args.intermediate_size, args.bias)
-        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-        self.post_attention_layernorm = nn.RMSNorm(
-            args.hidden_size, eps=args.rms_norm_eps
-        )
-
-    def __call__(
-        self,
-        x: mx.array,
-        mask: Optional[mx.array] = None,
-        cache: Optional[Any] = None,
-    ) -> mx.array:
-        r = self.self_attn(self.input_layernorm(x), mask, cache)
-        h = x + r
-        r = self.mlp(self.post_attention_layernorm(h))
-        out = h + r
-        return out
-
-
-class InternLM2Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        assert args.vocab_size > 0
-        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
-        self.layers = [
-            TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
-        ]
-        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        mask: mx.array = None,
-        cache=None,
-    ):
-        h = self.embed_tokens(inputs)
-
-        if mask is None:
-            mask = create_attention_mask(h, cache)
-
-        if cache is None:
-            cache = [None] * len(self.layers)
-
-        for layer, c in zip(self.layers, cache):
-            h = layer(h, mask, cache=c)
-
-        return self.norm(h)
-
-
-class Model(nn.Module):
-    def __init__(self, args: ModelArgs):
-        super().__init__()
-        self.args = args
-        self.model_type = args.model_type
-        self.model = InternLM2Model(args)
-        if not args.tie_word_embeddings:
-            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
-
-    def __call__(
-        self,
-        inputs: mx.array,
-        mask: mx.array = None,
-        cache=None,
-    ):
-        out = self.model(inputs, mask, cache)
-        if self.args.tie_word_embeddings:
-            out = self.model.embed_tokens.as_linear(out)
-        else:
-            out = self.lm_head(out)
-        return out
-
-    def sanitize(self, weights):
-        # Remove unused precomputed rotary freqs
-        return {k: v for k, v in weights.items() if "attention.rope.inv_freq" not in k}
-
-    @property
-    def layers(self):
-        return self.model.layers

llms/mlx_lm/tuner/datasets.py

@@ -170,7 +170,7 @@ def load_custom_hf_dataset(args, tokenizer: PreTrainedTokenizer):
         if prompt_feature and completion_feature:
             return CompletionsDataset(ds, tokenizer, prompt_feature, completion_feature)
         elif text_feature:
-            return Dataset(ds, tokenizer, text_key=text_feature)
+            return Dataset(train_ds, tokenizer, text_key=text_feature)
         else:
             raise ValueError(
                 "Specify either a prompt and completion feature or a text "

llms/mlx_lm/tuner/trainer.py

@@ -159,8 +159,8 @@ def evaluate(
         ntokens += toks
         mx.eval(all_losses, ntokens)
 
-    all_losses = mx.distributed.all_sum(all_losses, stream=mx.cpu)
+    all_losses = mx.distributed.all_sum(all_losses)
-    ntokens = mx.distributed.all_sum(ntokens, stream=mx.cpu)
+    ntokens = mx.distributed.all_sum(ntokens)
 
     return (all_losses / ntokens).item()
 
@@ -272,9 +272,9 @@ def train(
         if it % args.steps_per_report == 0 or it == args.iters:
             stop = time.perf_counter()
 
-            train_loss = mx.distributed.all_sum(losses, stream=mx.cpu).item()
+            train_loss = mx.distributed.all_sum(losses).item()
             train_loss /= steps * mx.distributed.init().size()
-            n_tokens = mx.distributed.all_sum(n_tokens, stream=mx.cpu).item()
+            n_tokens = mx.distributed.all_sum(n_tokens).item()
             learning_rate = optimizer.learning_rate.item()
             it_sec = args.steps_per_report / (stop - start)
             tokens_sec = float(n_tokens) / (stop - start)

llms/mlx_lm/tuner/utils.py

@@ -100,7 +100,6 @@ def linear_to_lora_layers(
         "minicpm",
         "deepseek",
         "olmo2",
-        "internlm3",
     ]:
         keys = set(["self_attn.q_proj", "self_attn.v_proj"])
         if model.model_type in ["mixtral", "phimoe"]:

llms/tests/test_finetune.py

@@ -21,7 +21,7 @@ from mlx_lm.tuner.utils import build_schedule
 @contextmanager
 def swapped_with_identity(obj, func):
     old_func = getattr(obj, func)
-    setattr(obj, func, lambda x, **kwargs: x)
+    setattr(obj, func, lambda x: x)
     yield
     setattr(obj, func, old_func)
 

llms/tests/test_models.py

@@ -927,23 +927,6 @@ class TestModels(unittest.TestCase):
             model, args.model_type, args.vocab_size, args.num_hidden_layers
         )
 
-    def test_internlm3(self):
-        from mlx_lm.models import internlm3
-
-        args = internlm3.ModelArgs(
-            model_type="internlm3",
-            hidden_size=1024,
-            num_hidden_layers=4,
-            intermediate_size=2048,
-            num_attention_heads=4,
-            rms_norm_eps=1e-5,
-            vocab_size=10_000,
-        )
-        model = internlm3.Model(args)
-        self.model_test_runner(
-            model, args.model_type, args.vocab_size, args.num_hidden_layers
-        )
-
 
 if __name__ == "__main__":
     unittest.main()